1. Field of the Invention
The present invention relates to a learning apparatus for building a network structure of a Bayesian network based on data obtained by learning (hereinafter may be referred to as “learning data”) and to a method of building the network structure.
2. Description of the Related Art
In recent years, the scope of application of the information processing technology has been extended, and an information-processing mechanism capable of operating according to various circumstances and a variety of users has become important. That is, treated objects having uncertainty (i.e., objects which may not be assumed in advance or may not be fully observed) have become increasingly important. Therefore, a mechanism of processing intelligent information to understand circumstances as precisely as possible even if only uncertain information is available and of performing appropriate processing may be required.
Because of these demands, a probability model which describes an object of interest using a network structure and probabilistically forecasts from observed events the object to be known has attracted attention. A Bayesian network which indicates a cause and effect relationship (connection) between nodes representing variables by means of a directed graph is known as a typical probability model.
[Non-patent reference 1] Cooper, G., and Herskovits, E., “A Bayesian method for the induction of probabilistic networks from Data”, Machine Learning, Vol. 9, pp. 309-347, 1992
[Non-patent reference 2] Hongjun Zhou, Shigeyuki Sakane, “Sensor Planning for Mobile Robot Localization Using Structure Learning and Inference of Bayesian Network,” Journal of the Japan Robotics Society, Vol. 22, No. 2, pp. 245-255, 2004